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Step by step guide for multiple time series forecasting in Python
Introduction
In the previous tutorial, I have demonstrated how you can use PyCaret to forecast time-series data using Machine Learning. If you haven’t read that yet, you can read Time Series Forecasting in Python tutorial before continuing with this one, as this tutorial builds upon some important concepts covered in the last tutorial.
Install Libraries
In this tutorial I will use PyCaret. Installing PyCaret is very easy and takes only a few minutes. I strongly recommend using a virtual environment to avoid potential conflicts with other libraries.
pip install pycaretDataset
In this tutorial, I will show the end-to-end implementation of multiple time-series forecasting using the Store Item Demand Forecasting Challenge dataset from Kaggle. This dataset has 10 different stores and each store has 50 items, i.e. total of 500 daily level time series data for five years (2013–2017).
